HFOs can be detected visually by neurologist expert [2] or automatically: using
automatic detectors developed on a preprocessing chain [3, 4].
To ensure the detection of HFOs, it is necessary to filter the used signal in HFO
bands, however spiky component can disturb the detection stage due to induced false
oscillations obtained by filter response [5]. Hence, it is necessary to choose accurate
filtering technique within this framework.
Bénar et al. proved the efficiency of stationary wavelet transform in detection and
separation between spikes and gamma oscillations.
Hence, we propose to study stationary wavelet transform performance in reconstruction of pure HFOs.
First, we tested SWT performance on filtered simulated data (in HFO frequency
range) where we evaluated it for different constraints (SNR, overlap rate, relative
amplitude and frequency range).
Second, our focus was to evaluate SWT robustness of HFO reconstruction on
filtered real IEEG signal (in HFO frequency range) using time frequency analysis.
These results would assist neurologist during diagnosis of pharmaco resistant patient by
defining epileptogenic tissue that should be delineated through a surgical intervention.
In the first section, we depict our simulated and real data, the filtering technique and
evaluation methods used. In the second section, we exhibit our obtained results and
finally we conclude and discuss our results.
2 Materials and Methods
2.1 Materials
All signal-processing steps of our paper are executed using Matlab software (Mathworks, Natick, MA) with EEGLAB toolbox.
Simulated Data: Obtained by a combination of a spike, and HFO shapes as real IEEG
signal, sampled at 1000 Hz. Through different tests, we created different sets of signal
(composed of spikes and HFO) by varying different parameters: relative amplitudes,
frequency of oscillations, signal to noise ratio (SNR) and overlapping rate: we obtained
4 sets of simulated data composed of spiky and HFO events. We increased the spiky
amplitude by 2, 4, 6, 8 and 10 times compared to oscillatory one. We varied also
oscillation’s frequency in this range [80 150 100 200 250] Hz (ripples and fast ripples).
Overlap between spike and HFO oscillations is changed with equal steps via the size of
oscillations window: no overlap (spike and oscillation are completely separated) until
we reached 100% overlap when spiky and oscillatory events are superimposed. The
overlap step is equal to 25%. Finally, we ranged SNR ratio (Eq. 1) from −5 dB to
20 dB.
SNR ¼ 10 Ã log S=N
ð
Þ
ð1Þ
Where S is the simulated signal and N is the studied added noise.
358
T. Guesmi et al.
automatic detectors developed on a preprocessing chain [3, 4].
To ensure the detection of HFOs, it is necessary to filter the used signal in HFO
bands, however spiky component can disturb the detection stage due to induced false
oscillations obtained by filter response [5]. Hence, it is necessary to choose accurate
filtering technique within this framework.
Bénar et al. proved the efficiency of stationary wavelet transform in detection and
separation between spikes and gamma oscillations.
Hence, we propose to study stationary wavelet transform performance in reconstruction of pure HFOs.
First, we tested SWT performance on filtered simulated data (in HFO frequency
range) where we evaluated it for different constraints (SNR, overlap rate, relative
amplitude and frequency range).
Second, our focus was to evaluate SWT robustness of HFO reconstruction on
filtered real IEEG signal (in HFO frequency range) using time frequency analysis.
These results would assist neurologist during diagnosis of pharmaco resistant patient by
defining epileptogenic tissue that should be delineated through a surgical intervention.
In the first section, we depict our simulated and real data, the filtering technique and
evaluation methods used. In the second section, we exhibit our obtained results and
finally we conclude and discuss our results.
2 Materials and Methods
2.1 Materials
All signal-processing steps of our paper are executed using Matlab software (Mathworks, Natick, MA) with EEGLAB toolbox.
Simulated Data: Obtained by a combination of a spike, and HFO shapes as real IEEG
signal, sampled at 1000 Hz. Through different tests, we created different sets of signal
(composed of spikes and HFO) by varying different parameters: relative amplitudes,
frequency of oscillations, signal to noise ratio (SNR) and overlapping rate: we obtained
4 sets of simulated data composed of spiky and HFO events. We increased the spiky
amplitude by 2, 4, 6, 8 and 10 times compared to oscillatory one. We varied also
oscillation’s frequency in this range [80 150 100 200 250] Hz (ripples and fast ripples).
Overlap between spike and HFO oscillations is changed with equal steps via the size of
oscillations window: no overlap (spike and oscillation are completely separated) until
we reached 100% overlap when spiky and oscillatory events are superimposed. The
overlap step is equal to 25%. Finally, we ranged SNR ratio (Eq. 1) from −5 dB to
20 dB.
SNR ¼ 10 Ã log S=N
ð
Þ
ð1Þ
Where S is the simulated signal and N is the studied added noise.
358
T. Guesmi et al.